mlsanityData to 7 Oct 2026

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network

arXiv 2202.10508 ↗First posted 21 Feb 2022cs.LGcited by 12

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Abstract (arXiv)

We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to destination (OD) travel demand and link flows of the network are available. Instead of estimating traffic flow patterns assuming certain user behavior (e.g., user equilibrium or system optimal), here we explore the idea of learning those flow patterns directly from the data. To implement this idea, we have formulated the traffic-assignment problem as a data-driven learning problem and developed a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. The proposed framework represents the transportation network and OD demand in an efficient way and utilizes the diffusion process of multiple OD demands from nodes to links. We validate the solutions of the model against analytical solutions generated from running static user equilibrium-based traffic assignments over Sioux Falls and East Massachusetts networks. The validation result shows that the implemented GCNN model can learn the flow patterns very well with less than 2% mean absolute difference between the actual and estimated link flows for both networks under varying congested conditions. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network. Hence this approach can overcome the challenges of deploying traffic assignment models over large-scale networks and open new directions of research in data-driven network modeling.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 13 of its 104 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

13 of 104 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  5. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  6. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  7. 2015Diffusion-Convolutional Neural Networkscited by 268
  8. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  9. 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Predictioncited by 67
  10. 2018Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Predictioncited by 61
  11. 2017Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approachcited by 35
  12. 2019Graph Markov Network for Traffic Forecasting with Missing Datacited by 14
1 more
  1. 2017Deep Learning applied to Road Traffic Speed forecastingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 2.

12 papers.

  1. 2023End-to-End Heterogeneous Graph Neural Networks for Traffic Assignmentcited by 13
  2. 2025Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networkscited by 4
  3. 2025Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignmentcited by 3
  4. 2024Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuationscited by 2
  5. 2025Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignmentcited by 2
  6. 2025From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problemcited by 1
  7. 2026Metalearning traffic assignment for network disruptions with graph convolutional neural networkscited by 1
  8. 2023Unsupervised Learning for Topological Classification of Transportation Networkscited by 0
  9. 2024A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networkscited by 0
  10. 2024A Survey on Differential Privacy for SpatioTemporal Data in Transportation Researchcited by 0
  11. 2025Learning from user's behaviour of some well-known congested traffic networkscited by 0
  12. 2026GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Modelscited by 0